• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.6
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• pp.509-519
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• 2007

This paper presents new techniques under development for monitoring the health and vibration of the active components in nuclear power plants, The purpose of this study is to develop an automated system for condition classification of a check valve one of the components being used extensively in a safety system of a nuclear power plant. Acoustic emission testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disc movement for valve failure such as wear and leakage due to foreign object interference in a check valve, It is clearly demonstrated that the evaluation of different types of failure types such as disc wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters, It is also shown that the leak size can be determined with an artificial neural network.

• Korean Journal of Organic Agriculture
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• v.25 no.1
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• pp.1-22
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• 2017

The purpose of this research is to analysis actual condition of promoting complex industrialization and changes after promoting, and then to classify organic farmer using cluster analysis based on delineated organic farmer's changes factors. This study also aims to investigate differences in socioeconomic characteristics and behavioral intentions among classified groups of organic farmer's changes. Data were obtained by questionnaire. From the factor analysis, four factors were derived as "Economics", "Public benefit", "Environment and philosophical values" and "Establishing management and the regional bedrock". From the cluster analysis, three cluster were derived as "group that has a positive awareness of promoting complex industry", "group that has a negative awareness of promoting complex industry" and "unstable management and a regional base". And the three classifications were significantly different in the satisfaction and behavioral intentions.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1167-1174
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• 2022

Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. The main drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operating environment. Even though ultrasonic non-destructive testing has been conducted in major nuclear components and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonic signals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonic technique (UT) and machine learning (ML) models. Several different ML models, specifically the K-nearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models, were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identified that the ML models can predict the category of ultrasonic signals effectively according to the aging condition.

• Journal of applied mathematics & informatics
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• v.1 no.1
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• pp.1-12
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• 1994

We consider a single channel CSMA/CD system with D homogeneous stations and impeded buffer of infinite size. We find a sufficient condition for the model to be stable under the threshold control policy and derive the limiting distri-bution of the number of messages in the system at the moment of service completion. We also derive the limiting distributing of the number of messages in the system size at arbitrary time by using Markov regenerative processes. Some numerical examples and special cases are also treated.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.162.4-162
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• 2001

This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.42.1-42
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• 2001

This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination. Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition. This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

• Journal of the Chungcheong Mathematical Society
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• v.37 no.2
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• pp.67-74
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• 2024

Maximal surfaces have a prominent place in the field of differential geometry, captivating researchers with their intriguing properties. Bearing a direct analogy to the minimal surfaces in Euclidean space, investigating both their similarities and differences has long been an important issue. This paper is aimed to give a local characterization of maximal surfaces in 𝕃³ in terms of their geodesic curvatures, which is analogous to the minimal surface case presented in [8]. We present a classification of the maximal surfaces under some simple condition on the geodesic curvatures of the parameter curves in the line of curvature coordinates.

• Database Research
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• v.34 no.3
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• pp.45-57
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• 2018

The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver's condition, but there is a limit to the generalized driver's condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver's condition. This paper confirms the effect of driver's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver's condition into three categories showed a classification accuracy of 96%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.685-691
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• 2009

The purpose of this paper is to classify the traffic condition in freeway by the impacts of road weather. The factor analysis showed that weather factors, which are considered as influential, are identified as weather condition (rain or clear), temperature and sight distance with RWIS and VDS data in Seohae bridge used. The result of ANOVA shows that weather is dividedinto clear and rainy; temperature into below and equal or above $5^{\circ}C$ and sight distance into below or equal or above 10km. Based on those factors, the freeway traffic condition has been classified as five different types. The flow-speed model for each traffic conditions was proposed, which was not significant due to the lack of smaple data. Although not sufficient, the methodology to categorize traffic situation model presented in this paper may shed light on the idea for the future and can be used for proper traffic management for each weather condition.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.578-582
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• 2005

In this study, the conceptual foundation and development process of the Antecedent soil Moisture Condition(AMC) in SCS runoff curve number method are reviewed. Although the runoff volume is very sensitive with AMC condition, the AMC class limits developed in SCS(1972) are used in rainfall-runoff analysis without careful consideration. Tn this study, following the SCS curve number development process, rainfall-runoff characteristics of the Jang-Pyung subbasin subject to the Pyung-Chang River basin are analyzed to evaluate the reasonability of the AMC class limits at present. The New AMC class limits are proposed by the sensitive analysis of the antecedent rainfall - curve number value. As a result, the classification value of AMC-I with II is 22mm of antecedent 5-day rainfall amount, and the classification of AMC-II with III is 117mm in growing season. When the New AMC class limits are applied to Jang-Pyung subbasin, AMC probability distribution shows that the AMC-II has increased remarkably even though the AMC-I has a little higher value. But the AMC-III has the smallest one. According to the conceptual basis of the curve number method, the AMC probability distribution, the New AMC class limits adopted, gives reasonable results.